| Type: | Undergraduate Subject |
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| Code: | DTSC13-302 |
| EFTSL: | 0.125 |
| Faculty: | Bond Business School |
| Semesters offered: |
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| Credit: | 10 |
| Study areas: |
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| Subject fees: |
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Description
The theory and practice of advanced regression techniques is the focus of this subject. Topics such as regularisation, limited dependent variable models, generalised linear models, random and mixed effects models, splines, additive models and tree-based regression will be covered. The programming language R will be used in this course.
Subject details
Learning outcomes
- Demonstrate knowledge of the limitations of normal linear regression models and the ability to appropriately interpret linear model outputs considering these limitations.
- Evaluate and appropriately choose between applications of a variety of linear and generalised linear regression modelling structures, including regularisation, dimension reduction and sequential variable selection.
- Apply non-normal dependent variable regression and classification models and identify their structural differences.
- Develop prediction models utilising splines, polynomials, recursive partitioning and general additive methods.
- Demonstrate ability to produce creative analytic solutions addressing a specified issue or problem.
- Demonstrate ability to verbally communicate the results of a statistical learning investigation.
Enrolment requirements
| Requisites: |
Nil |
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| Assumed knowledge: |
Assumed knowledge is the minimum level of knowledge of a subject area that students are assumed to have acquired through previous study. It is the responsibility of students to ensure they meet the assumed knowledge expectations of the subject. Students who do not possess this prior knowledge are strongly recommended against enrolling and do so at their own risk. No concessions will be made for students’ lack of prior knowledge. Assumed Prior Learning (or equivalent):Possess demonstrable knowledge in the theory and application of simple and multiple linear regression models to subject levels, such as Linear Models and Applied Econometrics, as well as basic data science concepts and techniques to subject levels, such as Data Science. |
| Restrictions: |
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Subject outlines
- May 2025 [Standard - Statistical Learning and Regression Models]
- May 2024 [Standard - Statistical Learning and Regression Models]
- May 2023 [Standard - Statistical Learning and Regression Models]
- May 2022 [Standard - Statistical Learning and Regression Models]
- May 2021 [Standard - Statistical Learning and Regression Models]
- May 2020 [Standard - Statistical Learning and Regression Models]
Subject dates
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May 2024
Standard Offering Enrolment opens: 17/03/2024 Semester start: 13/05/2024 Subject start: 13/05/2024 Last enrolment: 26/05/2024 Teaching census: 07/06/2024 Withdraw - Financial: 08/06/2024 Withdraw - Academic: 29/06/2024 -
May 2025
Standard Offering Enrolment opens: 23/03/2025 Semester start: 19/05/2025 Subject start: 19/05/2025 Last enrolment: 01/06/2025 Teaching census: 13/06/2025 Withdraw - Financial: 14/06/2025 Withdraw - Academic: 05/07/2025
| Standard Offering | |
|---|---|
| Enrolment opens: | 17/03/2024 |
| Semester start: | 13/05/2024 |
| Subject start: | 13/05/2024 |
| Last enrolment: | 26/05/2024 |
| Teaching census: | 07/06/2024 |
| Withdraw - Financial: | 08/06/2024 |
| Withdraw - Academic: | 29/06/2024 |
| Standard Offering | |
|---|---|
| Enrolment opens: | 23/03/2025 |
| Semester start: | 19/05/2025 |
| Subject start: | 19/05/2025 |
| Last enrolment: | 01/06/2025 |
| Teaching census: | 13/06/2025 |
| Withdraw - Financial: | 14/06/2025 |
| Withdraw - Academic: | 05/07/2025 |